Agricultural work machines may encounter obstacles, such as tires in the field, which may cause damage to the agricultural work machine. Other irregularities in the field, such as weeds, which can lead to a reduction in the quality of the harvested crop. Manually steering the agricultural work machine, for example during harvesting, thus requires the constant attention of the vehicle operator.
Automatic guidance, such as through use a GPS receiver or topographic maps, reduces the effort of steering the agricultural work machine. However, unknown new obstacles or current irregularities in the crop may be overlooked by these systems. Thus, the vehicle operator still needs to pay constant attention. In order to be warned of obstacles and irregularities early enough, local sensors are used, such as directly on the agricultural work machine, which monitor the environment.
EP 2 143 316 A1 discloses a system for early detection of harvest flow problems or foreign bodies in the harvest flow, and for partial automatic control of a self-driving harvesters, or their work elements. For this, the harvest flow is monitored by means of sensors, such as a camera. EP 2 143 316 A1 discloses two image evaluation methods. The first image evaluation method depicts irregularities in the harvest flow by means of motion blur. The second image evaluation method detects a displacement of crop features by means of an image comparison method, comparing successive images in order to detect foreign bodies.
US 2015/0319911 A1 discloses an analogous method to that in EP 2 143 316 A1. In addition, the method can detect landmarks in the environment.